Independent Researcher, USA.
Review Article
World Journal of Advanced Research and Reviews, 2022, 13(03), 629-638
Received on 15 February 2022; revised on 19 March 2022; accepted on 21 March 2022
With more and more companies moving to cloud platforms, adequate cloud security is the topmost priority for organizations today. Conventional security tools never identify sophisticated cyber-attacks, and thus AI-based real-time anomaly detection is the need of the hour. This research investigates the application of cutting-edge machine learning, deep learning, and security analytics in identifying and handling security anomalies from cloud logs. Our methodology utilizes hybrid AI models, federated learning, and graph neural networks to provide more accurate detection without breaching data privacy. Furthermore, the use of quantum-resilient cryptographic models and zero-trust principles further enhances cloud security. Cloud-native scalable technologies, decentralized security models, and real-time automated incident response systems are also utilized in this research, and hence, it is an end-to-end, adaptive, and high-performance security solution for multi-clouds.
Federated Learning; Quantum-Resilient Security; Zero-Trust AI; Graph Neural Networks; Self-Supervised Anomaly Detection
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Dhruvitkumar V Talati. Enhancing Multi-Cloud Security with Quantum-Resilient AI for Anomaly Detection. World Journal of Advanced Research and Reviews, 2022, 13(3), 629-638. Article DOI: https://doi.org/10.30574/wjarr.2022.13.3.0250